if calculus is the mathematics of continuous change, discrete mathematics is the mathematics of computation.
• logic
• sets
• relations
• graphs
• combinatorics
• recurrence
• counting
introductory discrete mathematics by v. k. balakrishnan teaches you to reason about structures made of distinct objects.
→ networks become graphs
→ programs become logical statements
→ recursion becomes recurrence
→ complex possibilities become counting problems
this is why discrete math sits underneath:
• algorithms
• data structures
• cryptography
• networks
• computer science
learn it not to memorize theorems.
learn it to think precisely about finite structures, relationships, and computation.
@allan9197 Right?! I think not knowing the history makes people fold experiences together that could be nevahseecomesee.
We made a day for all of us. It’s awesome for everyone to be more specific.
It’s not in our nature. We have other shit but please in the name of god don’t do this. 😂👎🏿
As a Trinidadian: No we do not.
We throw the West Indian Parade. If you is ah Trinidadian then you know culturally we reject ALL “Nevaseecomesee” behavior.
We have a parade. We getting ready to host the diaspora for Labor Day and “we need a Trinidadian Day”.
Grenada is the Mecca for Jab
Jab from everywhere: Jab Jab, Lanse Kod, Diablo, Shortknee, Bullpissle, Eshu, Blue Devil, Jab Molassie (Jumbalasee), etc.
Grenada made a home for us. I like it. 🥳🇬🇩
Paper from Meta shows Quantized reasoning models often lose because they keep doubting a correct answer instead of finishing.
Many of them reason well enough, but compression makes them hesitate at the wrong time.
The problem is that post-training quantization, a way to shrink models after training, can make reasoning models cheaper to run but worse at finishing cleanly.
The authors found that strong quantization does not only make models less capable, since in many failures the model already reached the right answer but then second-guessed itself.
Their core idea is that quantization adds noise at uncertain word choices, so the model becomes more likely to pick words like “wait,” “but,” or “alternatively” that reopen the problem.
They tested this across math, coding, and science tasks using 5 reasoning models, several quantization methods, and model sizes from 1.5B to 32B.
The main result is that aggressive quantization raised overthinking failures up to 52%, while a small penalty on 50 hesitation words cut reasoning length by 12% to 23% and often kept or improved accuracy.
Given compressed models are widely used to save memory and cost, very important to know that a very small decoding fix can stop many of them from wasting tokens and losing answers they already had.
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– arxiv. org/abs/2606.00206
Title: "Quantized Reasoning Models Think They Need to Think Longer, but They Do Not"